There exist systems and methods for analyzing the effects of medical treatments using cohort studies. Most of these systems and methods are based on randomized controlled trials of particular treatments. Given a sufficient number of subjects over a sufficient period of time, randomized controlled trials have the advantage of simplifying this analysis by evenly distributing confounding factors (or differences between the groups) across the group receiving the treatment and the control group. These systems and methods are limited because randomized controlled trials are expensive to conduct, they rely for their accuracy in part on a high number of test subjects, and they are not very effective at identifying secondary interactions resulting from conditions that are a-typical among treatment candidates. Additionally, randomized control trials, which are prospective cohort studies, cannot capitalize on the wealth of information available in the broader set of existing patient record data in the same way that retrospective cohort studies can.
Other existing systems and methods analyze historical patient record data to evaluate the effects of particular treatments. These systems are also limited because they use a largely manual modeling process to control for confounders and patient characteristics that are not evenly distributed in the historical data across the treated group and non-treated groups. Because of the high risk of bias in these manual confounder control processes, the quality of the results produced by the existing systems are highly dependent on the operator's expertise level. Additionally, main and interaction effects associated with a high number of potential confounders in large existing data sets makes manual modeling very time intensive and subject to human error.